Autonomous vehicle control using prior radar space map

ABSTRACT

Various technologies described herein pertain to controlling an autonomous vehicle to suppress data corresponding to predefined static objects in a radar output generated by a radar sensor system. A computing system of the autonomous vehicle retrieves prior data for a geographic location from a prior radar space map. The prior radar space map includes prior data for geographic locations in an environment corresponding to whether predefined static objects to be suppressed in radar outputs are located at the geographic locations. The computing system generates a score representative of a likelihood of a tracked object being at the geographic location based on data from the radar output for the geographic location, data from an output of a second sensor system for the geographic location, and the prior data for the geographic location from the prior radar space map. An engine, braking system, and/or steering system are controlled based on the score.

BACKGROUND

An autonomous vehicle is a motorized vehicle that can operate withouthuman conduction. An exemplary autonomous vehicle includes a pluralityof sensor systems, such as but not limited to, a radar sensor system, alidar sensor system, and an image (camera) sensor system, amongstothers. The autonomous vehicle operates based upon sensor signals outputby the sensor systems.

When operating in an environment, certain objects in the environment maycause one or more of the sensor systems of an autonomous vehicle togenerate outputs that include false positive data. The false positivedata can detrimentally impact conventional operation of an autonomousvehicle by causing the autonomous vehicle to unnecessarily stop for ormaneuver around certain types of objects. For instance, the types ofobjects that cause inclusion of false positive data in the generatedoutputs of sensor systems can be part of a roadway over which anautonomous vehicle drives or part of an overhanging structure underwhich an autonomous vehicle drives; in either case, it is desirable thatan autonomous vehicle need not change behavior due to existence of thesetypes of objects in the environment.

By way of illustration, various metallic objects can be in anenvironment in which an autonomous vehicle operates. These metallicobjects can be located at fixed geographic locations (e.g., the metallicobjects are not moving over time, and thus, are static). Moreover, themetallic objects may be at geographic locations in a path of anautonomous vehicle; yet, due to the nature of the metallic objects aswell as the position of the metallic objects, the autonomous vehicle maydrive over or under the metallic objects without the metallic objectsbeing impediments along the path traversed by the autonomous vehicle.Further, the static, metallic objects can strongly reflect radarsignals. Accordingly, a radar sensor system of an autonomous vehicle cangenerate a radar output that includes data corresponding to therelatively strong reflected signals from the static, metallic objects inthe environment. Thus, in operation, radar outputs that include datacorresponding to at least some of these static, metallic objects candetrimentally impact performance of the autonomous vehicle. Examples ofthe static, metallic objects include manhole covers, metallic plates,metallic grates, supporting structures of bridge overpasses, and thelike.

SUMMARY

The following is a brief summary of subject matter that is described ingreater detail herein. This summary is not intended to be limiting as tothe scope of the claims.

Described herein are various technologies that pertain to controlling anautonomous vehicle to suppress data corresponding to predefined staticobjects in a radar output generated by a radar sensor system. With morespecificity, described herein are various technologies pertaining toutilizing a prior radar space map that includes prior data forgeographic locations in an environment corresponding to whetherpredefined static objects to be suppressed in radar outputs are locatedat the geographic locations. Accordingly, prior data for a geographiclocation in an environment can be retrieved and utilized by theautonomous vehicle when generating a score representative of alikelihood of a tracked object being at a geographic location. Moreover,pursuant to various embodiments, described herein are various techniquesfor controlling behavior of the autonomous vehicle based on the priordata for geographic locations corresponding to the predefined staticobjects in the prior radar space map. The predefined static objects inthe environment can cause blind spots for the radar sensor systems(e.g., linear phase array radar sensor systems) by occluding portions offields of view of the radar sensor systems. Thus, movement of theautonomous vehicle can be controlled based on the prior data from theprior radar space map to position the autonomous vehicle to enable anotherwise occluded portion of a field of view of to be viewable by theradar sensor system.

According to various embodiments, an autonomous vehicle includes anengine, a braking system, a steering system, a radar sensor system, andat least a second sensor system. The radar sensor system generates aradar output. Moreover, the second sensor system, which is a differingtype of sensor system as compared to the radar sensor system, generatesa second output. For instance, the second sensor system can be a lidarsensor system, an image sensor system, or the like. The autonomousvehicle also includes a computing system that is in communication withthe engine, the braking system, the steering system, and the sensorsystems. The computing system can retrieve prior data for a geographiclocation in an environment from a prior radar space map. The prior radarspace map includes prior data for geographic locations in theenvironment corresponding to whether predefined static objects to besuppressed in radar outputs are located at the geographic locations.Further, the computing system can generate a score representative of alikelihood of a tracked object being at the geographic location. Thescore can be generated based on data from the radar output for thegeographic location, data from the second output for the geographiclocation, and the prior data for the geographic location from the priorradar space map. Moreover, the engine, the braking system, and/or thesteering system can be controlled based on the score representative ofthe likelihood of the tracked object being at the geographic location.

Pursuant to various embodiments, the computing system of the autonomousvehicle can detect a static object at a geographic location in theenvironment based on data from the radar output for the geographiclocation, where the radar output is generated by the radar sensor systemof the autonomous vehicle. A portion of a field of view of the radarsensor system beyond the geographic location from a perspective of theradar sensor system can be occluded due to the static object. Moreover,prior data for the geographic location can be retrieved by the computingsystem from a prior radar space map. Based on the prior data for thegeographic location from the prior space map, the computing system canidentify that the static object at the geographic location is apredefined static object to be suppressed in the radar outputs. Forinstance, the computing system can identify that the static object is amanhole cover, a metallic grate, a metallic plate, a supportingstructure for of a bridge overpass, or the like. Accordingly, the engineof the autonomous vehicle, the braking system of the autonomous vehicle,and/or the steering system of the autonomous vehicle can be controlledbased on the predefined static object to be suppressed being identifiedas being at the geographic location (e.g., reposition the autonomousvehicle to make the previously occluded portion of the field of view ofthe radar sensor system viewable).

The above summary presents a simplified summary in order to provide abasic understanding of some aspects of the systems and/or methodsdiscussed herein. This summary is not an extensive overview of thesystems and/or methods discussed herein. It is not intended to identifykey/critical elements or to delineate the scope of such systems and/ormethods. Its sole purpose is to present some concepts in a simplifiedform as a prelude to the more detailed description that is presentedlater.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a functional block diagram of an exemplary autonomousvehicle.

FIG. 2 illustrates an exemplary object detection system and prior radarspace map of the autonomous vehicle of FIG. 1 in greater detail.

FIGS. 3-4 illustrate exemplary top views of the autonomous vehicle ofFIG. 1.

FIG. 5 illustrates another functional block diagram of the exemplaryautonomous vehicle.

FIGS. 6-7 illustrate top views of an exemplary intersection.

FIG. 8 is a flow diagram that illustrates an exemplary methodology forcontrolling an autonomous vehicle to suppress data corresponding topredefined static objects in radar outputs generated by a radar sensorsystem of the autonomous vehicle.

FIG. 9 is a flow diagram that illustrates an exemplary methodology forcontrolling behavior of an autonomous vehicle based on prior data forgeographic locations corresponding to predefined static objects in aprior radar space map.

FIG. 10 illustrates an exemplary computing device.

DETAILED DESCRIPTION

Various technologies pertaining to controlling an autonomous vehicle tosuppress data corresponding to predefined static objects in radaroutputs generated by a radar sensor system are now described withreference to the drawings, wherein like reference numerals are used torefer to like elements throughout. In the following description, forpurposes of explanation, numerous specific details are set forth inorder to provide a thorough understanding of one or more aspects. It maybe evident, however, that such aspect(s) may be practiced without thesespecific details. In other instances, well-known structures and devicesare shown in block diagram form in order to facilitate describing one ormore aspects. Further, it is to be understood that functionality that isdescribed as being carried out by certain system components may beperformed by multiple components. Similarly, for instance, a componentmay be configured to perform functionality that is described as beingcarried out by multiple components.

Moreover, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom the context, the phrase “X employs A or B” is intended to mean anyof the natural inclusive permutations. That is, the phrase “X employs Aor B” is satisfied by any of the following instances: X employs A; Xemploys B; or X employs both A and B. In addition, the articles “a” and“an” as used in this application and the appended claims shouldgenerally be construed to mean “one or more” unless specified otherwiseor clear from the context to be directed to a singular form.

As used herein, the terms “component” and “system” are intended toencompass computer-readable data storage that is configured withcomputer-executable instructions that cause certain functionality to beperformed when executed by a processor. The computer-executableinstructions may include a routine, a function, or the like. It is alsoto be understood that a component or system may be localized on a singledevice or distributed across several devices. Further, as used herein,the term “exemplary” is intended to mean “serving as an illustration orexample of something.”

Referring now to the drawings, FIG. 1 illustrates an autonomous vehicle100. The autonomous vehicle 100 can navigate about roadways withouthuman conduction based upon sensor signals outputted by sensor systemsof the autonomous vehicle 100. The autonomous vehicle 100 includes aplurality of sensor systems, namely, a sensor system 1 102, . . . , anda sensor system N 104, where N can be substantially any integer greaterthan one (collectively referred to herein as sensor systems 102-104).The sensor systems 102-104 are of different types and are arranged aboutthe autonomous vehicle 100. For example, the sensor system 1 102 may bea radar sensor system and the sensor system N 104 may be an image(camera) sensor system. Other exemplary sensor systems included in thesensor systems 102-104 can include lidar sensor systems, GPS sensorsystems, sonar sensor systems, infrared sensor systems, and the like.

The autonomous vehicle 100 further includes several mechanical systemsthat are used to effectuate appropriate motion of the autonomous vehicle100. For instance, the mechanical systems can include, but are notlimited to, an engine 106, a braking system 108, and a steering system110. The engine 106 may be an electric engine or a combustion engine.The braking system 108 can include an engine brake, brake pads,actuators, and/or any other suitable componentry that is configured toassist in decelerating the autonomous vehicle 100. The steering system110 includes suitable componentry that is configured to control thedirection of movement of the autonomous vehicle 100.

The autonomous vehicle 100 additionally includes a computing system 112that is in communication with the sensor systems 102-104, the engine106, the braking system 108, and the steering system 110. The computingsystem 112 includes a processor 114 and memory 116; the memory 116includes computer-executable instructions that are executed by theprocessor 114. Pursuant to various examples, the processor 114 can be orinclude a graphics processing unit (GPU), a plurality of GPUs, a centralprocessing unit (CPU), a plurality of CPUs, an application-specificintegrated circuit (ASIC), a microcontroller, a programmable logiccontroller (PLC), a field programmable gate array (FPGA), or the like.

The computing system 112 can further include a data store 118. The datastore 118 includes a prior radar space map 120 that includes prior datafor geographic locations in an environment corresponding to whetherpredefined static objects to be suppressed in radar outputs are locatedat the geographic locations. The predefined static objects to besuppressed in the radar outputs can reflect radar signals. Moreover, thepredefined static objects to be suppressed in the radar outputs may beat geographic locations that are in paths of autonomous vehicles.However, due to the nature and positions of the predefined staticobjects, autonomous vehicles may drive over or under the predefinedstatic objects without the predefined static objects being impedimentsalong the paths traversed by the autonomous vehicles. For instance, thepredefined static objects to be suppressed can include various metallicobjects, which can reflect radar signals. Examples of the predefinedstatic objects to be suppressed in the radar outputs can include manholecovers, metallic plates, metallic grates, supporting structures ofbridge overpasses, and the like. Thus, if not suppressed, data in radaroutputs corresponding to the predefined static objects can yield falsepositive results, which can detrimentally impact operation of theautonomous vehicle 100. By way of illustration, if radar datacorresponding to a manhole cover is not suppressed, the computing system112 can improperly interpret the manhole cover as a car; thus, theautonomous vehicle 100 may be controlled to stop due to incorrectlyequating the radar data corresponding to the manhole cover as a car.

While many of the examples set forth herein describe the data store 118of the autonomous vehicle 100 including the prior radar space map 120,it is contemplated that a data store of a remote computing system (notshown) can additionally or alternatively include the prior radar spacemap 120. Pursuant to another example, it is to be appreciated that thedata store 118 of the autonomous vehicle 100 can include a portion ofthe prior radar space map 120 (e.g., the autonomous vehicle 100 canreceive the portion of the prior radar space map 120 from the remotecomputing system). The prior radar space map 120 can be generated bycollecting radar outputs in the environment over time, and identifyingthe predefined static object that are to be suppressed in the radaroutputs. Moreover, according to various examples, it is contemplatedthat the prior radar space map 120 can be utilized to localize theautonomous vehicle 100 (e.g., based on radar output generated by a radarsensor system).

The memory 116 of the computing system 112 includes an object detectionsystem 122 that is configured to generate a score representative of alikelihood of a tracked object being at a geographic location in anenvironment. The object detection system 122 can retrieve prior data forthe geographic location in the environment from the radar space map 120.Moreover, the object detection system 122 can generate the scorerepresentative of the likelihood of the tracked object being at thegeographic location based at least in part on the prior data for thegeographic location retrieved from the prior radar space map 120, aswill be described in greater detail below. Thus, if a predefined staticobject to be suppressed in radar outputs is identified as being at thegeographic location (e.g., as specified in the prior radar space map120), then the prior data for the geographic location can aid insuppressing the data from the radar output for the geographic location(which represents the relatively strong reflected signal attributable tothe predefined static object at the geographic location).

The memory 116 additionally includes a control system 124. The controlsystem 124 is configured to control at least one of the mechanicalsystems of the autonomous vehicle 100 (e.g., the engine 106, the brakingsystem 108, and/or the steering system 110). The control system 124 cancontrol the mechanical system(s) based upon the score representative ofthe likelihood of the tracked object being at the geographic location asdetermined by the object detection system 122.

Exemplary operation of the autonomous vehicle 100 is now set forth. Aradar sensor system (e.g., the sensor system 1 102) can generate a radaroutput. Additionally, a second sensor system (e.g., the sensor system N104), which is a differing type of sensor system as compared to theradar sensor system, can generate a second output. The second sensorsystem, for instance, can be an image sensor system or a lidar sensorsystem. The object detection system 122 can retrieve prior data for ageographic location in an environment from the prior radar space map120. Moreover, the object detection system 122 can generate a scorerepresentative of a likelihood of a tracked object being at thegeographic location based on data from the radar output for thegeographic location, data from the second output for the geographiclocation, and the prior data for the geographic location. Further, thecontrol system 124 can control the mechanical system(s) based upon thescore representative of the likelihood of the tracked object being atthe geographic location.

According to various examples, it is to be appreciated that a thirdsensor system (e.g., one of the sensor systems 102-104) can generate athird output. The third sensor system again can be a differing type ofsensor system as compared to the radar sensor system and the secondsensor system. Moreover, the object detection system 122 can generatethe score representative of the likelihood of the tracked object beingat the geographic location further based on data from the third outputfor the geographic location. Pursuant to an illustration, the radarsensor system can generate a radar output, a lidar sensor system cangenerate a lidar output, and an image sensor system can generate animage output. Following this illustration, the object detection system122 can generate a score representative of a likelihood of a trackedobject being at a geographic location based on data from the radaroutput for the geographic location, data from the lidar output for thegeographic location, data from the image output for the geographiclocation, and prior data for the geographic location retrieved from theprior radar space map 120.

According to various examples, a tracked object to be detected by theobject detection system 122 can be a car, a truck, or a bus; however,the claimed subject matter is not so limited. Moreover, the objectdetection system 122 desirably ignores (or diminishes consideration of)data in a radar output corresponding to predefined static objects to besuppressed when generating a score representative of a likelihood of atracked object being at a geographic location. However, in contrast tosome conventional approaches that ignore data corresponding to allstatic objects in radar outputs, radar data corresponding to staticobjects other than the predefined static objects to be suppressed isutilized by the object detection system 122 to generate a scorerepresentative of a likelihood of a tracked object being at a geographiclocation. By way of illustration, if a parked car is at a particulargeographic location, radar data from a radar output for the particulargeographic location can be employed by the object detection system 122to generate a score representative of the likelihood of a tracked objectbeing at the particular geographic location (e.g., without suppressionof such radar data, assuming that a predefined static object is not alsopositioned at the particular geographic location).

Now turning to FIG. 2, illustrated are the object detection system 122and the prior radar space map 120 of the autonomous vehicle 100 ingreater detail. As described above, the sensor systems 102-104 of theautonomous vehicle 100 can generate respective outputs. Thus, forinstance, the object detection system 122 can receive radar output 202generated by the radar sensor system of the autonomous vehicle 100,lidar output 204 generated by the lidar sensor system of the autonomousvehicle 100, and an image output 206 generated by the image sensorsystem of the autonomous vehicle 100. Moreover, as set forth above, theobject detection system 122 can generate a score 208 representative of alikelihood of a tracked object being at a geographic location. The score208 can be generated by the object detection system 122 based on theradar output 202, the lidar output 204, the image output 206, and priordata from the prior radar space map 120.

The object detection system 122 includes a static object identificationcomponent 210. The static object identification component 210 isconfigured to detect a static object at a geographic location based ondata from the radar output 202 for a geographic location. By way ofillustration, the static object identification component 210 can detecta static object at a geographic location based on Doppler shift datacorresponding to the geographic location in the radar output 202. TheDoppler shift data can be indicative of a relative velocity of theobject relative to the autonomous vehicle 100. Accordingly, if thestatic object identification component 210 identifies that a detectedobject at a particular geographic location is not moving (e.g., thedetected object is identified as having a velocity of zero, the detectedobject is identified as having a velocity below a threshold velocity),then the static object identified component 210 can deem the detectedobject to be a static object.

When the static object identification component 210 detects a staticobject at a geographic location, the object detection system 122 canretrieve prior data corresponding to the geographic location from theprior radar space map 120. The prior radar space map 120 may includeprior data corresponding to the geographic location (if a predefinedstatic object to be suppressed in radar outputs is located at thegeographic location). Otherwise, if a predefined static object to besuppressed in radar outputs is not located at the geographic location,the prior radar space map 120 may lack prior data corresponding to thegeographic location. According to another example, if a predefinedstatic object to be suppressed in radar outputs is not located at thegeographic location, the prior radar space map 120 may include priordata corresponding to the geographic location that does not cause radardata for such geographic location to be suppressed. Further, it iscontemplated that when the static object identification component 210does not detect a static object at a geographic location based on thedata from the radar output 202 for the geographic location, the objectdetection system 122 need not retrieve prior data for the geographiclocation from the prior radar space map 120.

The object detection system 122 further includes a fusion component 212configured to combine data from the sensor systems as well as prior datafrom the prior radar space map 120 (e.g., if retrieved and if the priorradar space map 120 includes corresponding prior data) to generate thescore 208. According to an example, the fusion component 212 can be aBayesian system that is utilized to generate the score 208.

According to an example, when the static object identification component210 detects a static object at a geographic location, the objectdetection system 122 can retrieve prior data corresponding to thegeographic location from the prior radar space map 120. Following thisexample, the fusion component 212 can generate the score 208representative of a likelihood of a tracked object being at thegeographic location based on data from the radar output 202 for thegeographic location, data from the lidar output 204 for the geographiclocation, data from the image output 206 for the geographic location,and the prior data from the prior radar space map 120 corresponding tothe geographic location.

Pursuant to another example, the static object identification component210 does not detect a static object at a geographic location. Followingthis example, the fusion component 212 can generate the score 208representative of the likelihood of a tracked object being at thegeographic location based on data from the radar output 202 for thegeographic location, data from the lidar output 204 for the geographiclocation, and data from the image output 206 for the geographic location(without prior data for the geographic location from the prior radarspace map 120).

The following example is set forth for illustration purposes. It iscontemplated that the score 208 representative of a likelihood of atracked object being at a geographic location (e.g., probability of acar being at the geographic location) can be generated by the fusioncomponent 212 as follows:P(car)=f(P(radar),P(lidar),P(vision))Accordingly, the probably of a car being at the geographic location canbe a function of a probability from the radar output 202 (P(radar)), aprobability from the lidar output 204 (P(lidar)), and a probability fromthe image output 206 (P(vision)). When an object such as a manholecover, a metallic plate, a metallic grate, or a supporting structure ofa bridge overpass is at the geographic location, the probability fromthe radar output 202 (P(radar)) can be increased, which can cause afalse positive detection of a car at the geographic location (e.g.,P(car) can be increased). To handle such scenarios, some conventionalapproaches ignore the probability from the radar output 202 for staticobjects. However, with these traditional approaches, the probabilityfrom the radar output 202 for non-moving objects such as cars, trucks,or buses are not considered; instead, the data from the lidar output 204and the data from the image output 206 are relied upon for theseconventional techniques to detect the non-moving cars, trucks, buses,etc. In contrast, as set forth herein, prior data from the prior radarspace map 120 is utilized to suppress predefined static objects in radaroutputs; thus, the probability of the car being at the geographiclocation can further be computed by the fusion component 212 based onthe prior data corresponding to the geographic location (e.g., P(radar)may be a function of the prior data corresponding to the geographiclocation, P(car) may further be a function of the prior data).

Now turning to FIG. 3, illustrated is a top view 300 of the autonomousvehicle 100 that is traveling on a road 302. The road 302 includes amanhole cover 302 at a particular geographic location. As depicted, themanhole cover 302 can be within a field of view 306 of the radar sensorsystem of the autonomous vehicle 100. Radar output generated by theradar sensor system can include data corresponding to the manhole cover302 at the particular geographic location. According to the techniquesdescribed herein, it can be desirable to suppress the data correspondingto the manhole cover 302 at the particular geographic location in theradar output when determining a score representative of a likelihood ofa tracked object being at the particular geographic location. Thus,assuming that the prior radar space map 120 includes prior datacorresponding to the manhole cover 302 at the particular geographiclocation, the prior data can be utilized to suppress the datacorresponding to the manhole cover 302 at the particular geographiclocation when generating the score (e.g., which can decrease thelikelihood of the manhole cover 304 improperly being interpreted as acar). By using the prior data corresponding to the manhole cover 302 atthe particular geographic location to generate the score representativeof the likelihood of the tracked object being at the particulargeographic location, the autonomous vehicle 100 need not be controlledto unnecessarily stop or maneuver around the manhole cover 304 on theroad 302.

With reference to FIG. 4, illustrated is another top view 400 of theautonomous vehicle 100 that is traveling on a road 402. As depicted, anon-moving car 404 is at a particular geographic location, which iswithin a field of view 406 of the radar sensor system of the autonomousvehicle 100 (e.g., the car 404 can be parked or otherwise stopped at theparticular geographic location). Radar output generated by the radarsensor system can include data corresponding to the non-moving car 404at the particular geographic location.

According to an example, a predefined static object to be suppressed inradar outputs is not also positioned at the particular geographiclocation at which the car 404 is positioned. Following this example,data from the radar output corresponding to the car 404 at theparticular geographic location is used to generate a scorerepresentative of a likelihood of a tracked object being at theparticular geographic location. For instance, it is contemplated thatthe prior radar space map 120 may lack prior data corresponding to theparticular geographic location due to a predefined static object to besuppressed in radar outputs not being located at the particulargeographic location. Pursuant to another illustration, prior datacorresponding to the particular geographic location can be retrievedfrom the prior radar space map 120; however, the retrieved prior datacorresponding to the particular geographic location may not suppress thedata from the radar output corresponding to the car 404 at theparticular geographic location due to a predefined static object to besuppressed in radar outputs not being located at the particulargeographic location.

In accordance with another example, a predefined static object to besuppressed in radar outputs is positioned at the particular geographiclocation at which the car 404 is positioned. Pursuant to this example,data from the radar outputs corresponding to the car 404 (as well as thepredefined static object such as a manhole cover as in the example ofFIG. 3) is suppressed when determining a score representative of alikelihood of a tracked object being at the particular geographiclocation (e.g., the prior data corresponding to the predefined staticobject to be suppressed at the particular geographic location is used tosuppress the predefined static object). Accordingly, data from the othersensor systems (e.g., data from the lidar sensor system and/or data fromthe image sensor system) can be utilized to detect the non-moving car404 at the particular geographic location.

With reference to FIG. 5, illustrated is another exemplary embodiment ofthe autonomous vehicle 100. The autonomous vehicle 100 includes thesensor systems 102-104, the mechanical systems (e.g., the engine 106,the braking system 108, and the steering system 110) and the computingsystem 112 as described above. Again, the memory 116 of the computingsystem 112 can include the object detection system 122 and the controlsystem 124. Moreover, the memory 116 can include a view managementsystem 502 configured to control a position of the autonomous vehicle110 based on prior data for geographic locations in the prior radarspace map 120.

Radar output generated by a radar sensor system (e.g., one of the sensorsystems 102-104) can be received. As described above, the objectdetection system 122 (e.g., the static object identification component210) can detect a static object at a geographic location in anenvironment based on data from the radar output for the geographiclocation. Moreover, a portion of the field of view of the radar sensorsystem beyond the geographic location from a perspective of the radarsensor system can be occluded due to the static object. For instance,such occlusion can result from use of a linear phase array radar sensorsystem (e.g., a one-dimensional phase array radar sensor system); by wayof illustration, a two-dimensional phase array radar sensor system maynot similarly encounter occlusion. The view management system 502 canretrieve prior data for the geographic location from the prior radarspace map 120. Further, the view management system 502 can identify,based on the prior data for the geographic location from the prior radarspace map 120, that the static object at the geographic location is apredefined static object to be suppressed in the radar outputs. The viewmanagement system 502 can further identify that the static object at thegeographic location is the predefined static object to be suppressed inthe radar outputs based on a score (e.g., the score 208) representativeof a likelihood of a tracked object being at the geographic locationgenerated by the object detection system 122. The view managementcomponent 502 can further cause the control system 124 to control atleast one of the engine 106, the braking system 108, or the steeringsystem 110 of the autonomous vehicle 100 based on the predefined staticobject to be suppressed being at the geographic location.

According to an illustration, the view management component 502 cancause the control system 124 to control the engine 106, the brakingsystem 108, and/or the steering system 110 to reposition the autonomousvehicle 100 based on occlusion in the field of view of the radar sensorsystem caused by the predefined static object to be suppressed.Additionally or alternatively, the view management system 502 can causethe control system 124 to control the engine 106, the braking system 108and/or the steering system 110 to reposition the autonomous vehicle 100such that the predefined static object to be suppressed at thegeographic location is removed from the field of view of the radarsensor system. Accordingly, the view management system 502 can provideoutput to the control system 124 to control the engine 106, the brakingsystem 108, and/or the steering system 110.

With reference to FIG. 6, illustrated is a top view of an intersection600. The autonomous vehicle 100 approaches the intersection 600 in adirection represented by an arrow 602. Moreover, the autonomous vehicle100 will be turning left so as to travel in a direction represented byan arrow 604. Moreover, a manhole cover 606 is positioned in theintersection 600. When the autonomous vehicle 100 is at the positiondepicted in FIG. 6, the manhole cover 606 can occlude a portion of afield of view 608 of a radar sensor system of the autonomous vehicle 100beyond the geographic location of the manhole cover 606 from aperspective of the radar sensor system. Thus, the radar sensor system ofthe autonomous vehicle 100 can have a blind spot beyond the geographiclocation of the manhole cover 606; accordingly, the radar sensor systemmay be unable to be used to detect oncoming cars traveling in adirection represented by an arrow 610 when at the position depicted inFIG. 6.

Turning to FIG. 7, illustrated is another top view of the intersection600. The autonomous vehicle 100 can be controlled to move based on priordata for the geographic location from the prior radar space map 120. Inparticular, based on the prior data for the geographic locationcorresponding to the manhole cover 606 to be suppressed in radaroutputs, the autonomous vehicle 100 can be controlled to move. Theautonomous vehicle 100 can be controlled to be repositioned based on theocclusion in the field of view 608 of the radar sensor system caused bythe manhole cover 608. For instance, as depicted, the autonomous vehicle100 can be controlled to be repositioned such that the manhole cover 606at the geographic location is removed from the field of view 608 of theradar sensor system. Thus, when in the position shown in FIG. 7, theradar sensor system of the autonomous vehicle 100 can be used to detectoncoming cars traveling in the direction represented by the arrow 610.

FIGS. 8-9 illustrate exemplary methodologies relating to controlling anautonomous vehicle utilizing a prior radar space map that includes priordata for geographic locations in an environment corresponding to whetherpredefined static objects to be suppressed in radar outputs are locatedat the geographic locations. While the methodologies are shown anddescribed as being a series of acts that are performed in a sequence, itis to be understood and appreciated that the methodologies are notlimited by the order of the sequence. For example, some acts can occurin a different order than what is described herein. In addition, an actcan occur concurrently with another act. Further, in some instances, notall acts may be required to implement a methodology described herein.Accordingly, the autonomous vehicle 100 can be controlled to improve aview by mitigating occlusion caused by predefined static object(s) to besuppressed in radar outputs specified in the prior radar space map 120.

Moreover, the acts described herein may be computer-executableinstructions that can be implemented by one or more processors and/orstored on a computer-readable medium or media. The computer-executableinstructions can include a routine, a sub-routine, programs, a thread ofexecution, and/or the like. Still further, results of acts of themethodologies can be stored in a computer-readable medium, displayed ona display device, and/or the like.

FIG. 8 illustrates a methodology 800 for controlling an autonomousvehicle to suppress data corresponding to predefined static objects inradar outputs generated by a radar sensor system of the autonomousvehicle. At 802, prior data for a geographic location in an environmentcan be retrieved from a prior radar space map. The prior radar space mapincludes prior data for geographic locations in the environmentcorresponding to whether predefined static objects to be suppressed inradar outputs are located at the geographic locations. At 804, a scorerepresentative of a likelihood of a tracked object being at thegeographic location can be generated. The score can be generated basedon data from a radar output for the geographic location generated by theradar sensor system of the autonomous vehicle, data from a second outputfor the geographic location generated by a second sensor system of theautonomous vehicle, and the prior data for the geographic location fromthe prior radar space map. The second sensor system is a differing typeof sensor system as compared to the radar sensor system (e.g., a lidarsensor system, an image sensor system). Moreover, it is contemplatedthat data from a third output for the geographic location generated by athird sensor system of the autonomous vehicle can also be used togenerate the score; the third sensor system can be a differing type ofsensor system as compared to the radar sensor system and the secondsensor system. At 806, at least one of an engine of the autonomousvehicle, a braking system of the autonomous vehicle, or a steeringsystem of the autonomous vehicle can be controlled based upon the scorerepresentative of the likelihood of the tracked object being at thegeographic location.

Turning to FIG. 9, illustrated is a methodology 900 for controllingbehavior of an autonomous vehicle based on prior data for geographiclocations corresponding to predefined static objects in a prior radarspace map. At 902, a static object can be detected at a geographiclocation in an environment based on data from a radar output for thegeographic location. For instance, the radar output can be received froma radar sensor system of the autonomous vehicle. Moreover, a portion ofa field of view of the radar sensor system beyond the geographiclocation from a perspective of the radar sensor system can be occludeddue to the static object. At 904, prior data for the geographic locationcan be retrieved from the prior radar space map. The prior radar spacemap can include prior data for geographic locations in the environmentcorresponding to whether predefined static objects to be suppressed inradar outputs are located at the geographic locations. At 906, adetermination can be made concerning whether the static object at thegeographic location is a predefined static object to be suppressed inthe radar outputs. For instance, based on the prior data for thegeographic location from the prior radar space map, the static object atthe geographic location can be identified as a predefined static objectto be suppressed in the radar outputs. At 908, at least one of an engineof the autonomous vehicle, a braking system of the autonomous vehicle,or a steering system of the autonomous vehicle can be controlled basedon the predefined static object to be suppressed being at the geographiclocation. For example, the autonomous vehicle can be repositioned basedon occlusion in the field of view of the radar sensor system caused bythe predefined static object to be suppressed.

Referring now to FIG. 10, a high-level illustration of an exemplarycomputing device 1000 that can be used in accordance with the systemsand methodologies disclosed herein is illustrated. For instance, thecomputing device 1000 may be or include the computing system 112. Thecomputing device 1000 includes at least one processor 1002 (e.g., theprocessor 114) that executes instructions that are stored in a memory1004 (e.g., the memory 116). The instructions may be, for instance,instructions for implementing functionality described as being carriedout by one or more systems discussed above or instructions forimplementing one or more of the methods described above. The processor1002 may be a GPU, a plurality of GPUs, a CPU, a plurality of CPUs, amulti-core processor, etc. The processor 1002 may access the memory 1004by way of a system bus 1006. In addition to storing executableinstructions, the memory 1004 may also store sensor system outputs(e.g., the radar output 202, the lidar output 204, the image output206), scores generated by the object detection system 122 (e.g., thescore 208), the prior radar space map 120, and so forth.

The computing device 1000 additionally includes a data store 1008 (e.g.,the data store 118) that is accessible by the processor 1002 by way ofthe system bus 1006. The data store 1008 may include executableinstructions, sensor system outputs (e.g., the radar output 202, thelidar output 204, the image output 206), scores generated by the objectdetection system 122 (e.g., the score 208), the prior radar space map120, etc. The computing device 1000 also includes an input interface1010 that allows external devices to communicate with the computingdevice 1000. For instance, the input interface 1010 may be used toreceive instructions from an external computer device, etc. Thecomputing device 1000 also includes an output interface 1012 thatinterfaces the computing device 1000 with one or more external devices.For example, the computing device 1000 may transmit control signals tothe engine 106, the braking system 108, and/or the steering system 110by way of the output interface 1012.

Additionally, while illustrated as a single system, it is to beunderstood that the computing device 1000 may be a distributed system.Thus, for instance, several devices may be in communication by way of anetwork connection and may collectively perform tasks described as beingperformed by the computing device 1000.

Various functions described herein can be implemented in hardware,software, or any combination thereof. If implemented in software, thefunctions can be stored on or transmitted over as one or moreinstructions or code on a computer-readable medium. Computer-readablemedia includes computer-readable storage media. A computer-readablestorage media can be any available storage media that can be accessed bya computer. By way of example, and not limitation, suchcomputer-readable storage media can comprise RAM, ROM, EEPROM, CD-ROM orother optical disk storage, magnetic disk storage or other magneticstorage devices, or any other medium that can be used to store desiredprogram code in the form of instructions or data structures and that canbe accessed by a computer. Disk and disc, as used herein, includecompact disc (CD), laser disc, optical disc, digital versatile disc(DVD), floppy disk, and blu-ray disc (BD), where disks usually reproducedata magnetically and discs usually reproduce data optically withlasers. Further, a propagated signal is not included within the scope ofcomputer-readable storage media. Computer-readable media also includescommunication media including any medium that facilitates transfer of acomputer program from one place to another. A connection, for instance,can be a communication medium. For example, if the software istransmitted from a website, server, or other remote source using acoaxial cable, fiber optic cable, twisted pair, digital subscriber line(DSL), or wireless technologies such as infrared, radio, and microwave,then the coaxial cable, fiber optic cable, twisted pair, DSL, orwireless technologies such as infrared, radio and microwave are includedin the definition of communication medium. Combinations of the aboveshould also be included within the scope of computer-readable media.

Alternatively, or in addition, the functionality described herein can beperformed, at least in part, by one or more hardware logic components.For example, and without limitation, illustrative types of hardwarelogic components that can be used include Field-programmable Gate Arrays(FPGAs), Program-specific Integrated Circuits (ASICs), Program-specificStandard Products (ASSPs), System-on-a-chip systems (SOCs), ComplexProgrammable Logic Devices (CPLDs), etc.

What has been described above includes examples of one or moreembodiments. It is, of course, not possible to describe everyconceivable modification and alteration of the above devices ormethodologies for purposes of describing the aforementioned aspects, butone of ordinary skill in the art can recognize that many furthermodifications and permutations of various aspects are possible.Accordingly, the described aspects are intended to embrace all suchalterations, modifications, and variations that fall within the scope ofthe appended claims. Furthermore, to the extent that the term “includes”is used in either the details description or the claims, such term isintended to be inclusive in a manner similar to the term “comprising” as“comprising” is interpreted when employed as a transitional word in aclaim.

What is claimed is:
 1. An autonomous vehicle, comprising: an engine; abraking system; a steering system; a radar sensor system that generatesa radar output; a second sensor system that generates a second output,the second sensor system being a differing type of sensor system ascompared to the radar sensor system; and a computing system that is incommunication with the engine, the braking system, the steering system,the radar sensor system, and the second sensor system, wherein thecomputing system comprises: a processor; and memory that storescomputer-executable instructions that, when executed by the processor,cause the processor to perform acts comprising: detecting a staticobject at a geographic location in an environment based on data from theradar output for the geographic location; retrieving prior data forgeographic location in the environment from a prior radar space map, theprior radar space map comprises prior data for geographic locations inthe environment corresponding to whether predefined static objects to besuppressed in radar outputs are located at the geographic locations;generating a score representative of a likelihood of a tracked objectbeing at the geographic location based on the data from the radar outputfor the geographic location, data from the second output for thegeographic location, and the prior data for the geographic location,wherein the data from the radar output for the geographic locationcorresponding to the static object is selectively suppressed forgenerating the score representative of the likelihood of the trackedobject being at the geographic location based on whether the staticobject is a predefined static object to be suppressed as specified bythe prior data for the geographic location; and controlling at least oneof the engine, the braking system, or the steering system based upon thescore representative of the likelihood of the tracked object being atthe geographic location.
 2. The autonomous vehicle of claim 1, whereinthe second sensor system is a lidar sensor system.
 3. The autonomousvehicle of claim 1, wherein the second sensor system is an image sensorsystem.
 4. The autonomous vehicle of claim 1, further comprising: athird sensor system that generates a third output, the third sensorsystem being a differing type of sensor system as compared to the radarsensor system and the second sensor system; wherein the score is furthergenerated based on data from the third output for the geographiclocation.
 5. The autonomous vehicle of claim 1, wherein the predefinedstatic objects to be suppressed in the radar outputs comprise manholecovers.
 6. The autonomous vehicle of claim 1, wherein the predefinedstatic objects to be suppressed in the radar outputs comprise metallicplates and metallic grates.
 7. The autonomous vehicle of claim 1,wherein the predefined static objects to be suppressed in the radaroutputs comprise supporting structures of bridge overpasses.
 8. Theautonomous vehicle of claim 1, wherein: when a static object is notdetected at a differing geographic location based on data from the radaroutput for the differing geographic location: prior data for thediffering geographic location is not retrieved from the prior radarspace map; and a differing score representative of a likelihood of atracked object being at the differing geographic location is generatedbased on the data from the radar output for the differing geographiclocation and data from the second output for the differing geographiclocation without the prior data for the differing geographic locationfrom the prior radar space map.
 9. The autonomous vehicle of claim 1,wherein the score is generated utilizing a Bayesian system.
 10. Theautonomous vehicle of claim 1, wherein the tracked object is one of acar, a truck, or a bus.
 11. The autonomous vehicle of claim 1, wherein aportion of a field of view of the radar sensor system beyond thegeographic location at which the static object is detected is occludedfrom a perspective of the radar sensor system due to the static object,and wherein the memory further stores computer-executable instructionsthat, when executed by the processor, cause the processor to performacts comprising: identifying, based on the score representative of thelikelihood of the tracked object being at the geographic location, thatthe static object at the geographic location is the predefined staticobject to be suppressed in the radar outputs; wherein controlling the atleast one of the engine, the braking system, or the steering systembased upon the score representative of the likelihood of the trackedobject being at the geographic location further comprises controllingthe at least one of the engine, the braking system, or the steeringsystem based on the predefined static object to be suppressed being atthe geographic location.
 12. The autonomous vehicle of claim 11, whereinthe at least one of the engine, the braking system, or the steeringsystem are controlled to reposition the autonomous vehicle based onocclusion in the field of view of the radar sensor system caused by thepredefined static object to be suppressed such that the portion of thefield of view occluded by the predefined static object becomes viewableby the radar sensor system when the autonomous vehicle is repositioned.13. The autonomous vehicle of claim 1, wherein the predefined staticobjects to be suppressed in the radar outputs are passable by vehiclesone of over or under without impeding the vehicles.
 14. A methodperformed by an autonomous vehicle, the method comprising: receiving aradar output generated by a radar sensor system of the autonomousvehicle; detecting a static object at a geographic location in anenvironment based on data from the radar output for the geographiclocation, wherein a portion of a field of view of the radar sensorsystem beyond the geographic location from a perspective of the radarsensor system is occluded due to the static object; retrieving priordata for the geographic location from a prior radar space map, the priorradar space map comprises prior data for geographic locations in theenvironment corresponding to whether predefined static objects to besuppressed in radar outputs are located at the geographic locations;identifying, based on the prior data for the geographic location fromthe prior radar space map, that the static object at the geographiclocation is a predefined static object to be suppressed in the radaroutputs; and controlling at least one of an engine of the autonomousvehicle, a braking system of the autonomous vehicle, or a steeringsystem of the autonomous vehicle based on the predefined static objectto be suppressed being at the geographic location.
 15. The method ofclaim 14, wherein the at least one of the engine, the braking system, orthe steering system are controlled to reposition the autonomous vehiclebased on occlusion in the field of view of the radar sensor systemcaused by the predefined static object to be suppressed.
 16. The methodof claim 14, wherein the at least one of the engine, the braking system,or the steering system are controlled to reposition the autonomousvehicle such that the predefined static object to be suppressed at thegeographic location is removed from the field of view of the radarsensor system.
 17. The method of claim 14, further comprising: receivinga second output generated by a second sensor system of the autonomousvehicle, the second sensor system being a different type of sensorsystem as compared to the radar sensor system; and generating a scorerepresentative of a likelihood of a tracked object being at thegeographic location, the score being generated based on the data fromthe radar output for the geographic location, data from the secondoutput for the geographic location, and the prior data for thegeographic location, wherein the data from the radar output for thegeographic location corresponding to the static object is selectivelysuppressed for generating the score representative of the likelihood ofthe tracked object being at the geographic location based on whether thestatic object is a predefined static object to be suppressed asspecified by the prior data for the geographic location.
 18. The methodof claim 17, wherein the second sensor system is one of a lidar sensorsystem or an image sensor system.
 19. The method of claim 14, whereinthe predefined static objects to be suppressed in the radar outputscomprise at least one of: manhole covers; metallic plates; metallicgrates; or supporting structures of bridge overpasses.
 20. An autonomousvehicle, comprising: a computer-readable storage medium that comprisesinstructions that, when executed by one of more processors, cause theone or more processors to perform actions comprising: detecting a staticobject at a geographic location in an environment based on data from aradar output for the geographic location, the radar output beinggenerated by a radar sensor system of the autonomous vehicle; retrievingprior data for the geographic location from a prior radar space map, theprior radar space map comprises prior data for geographic locations inthe environment corresponding to whether predefined static objects to besuppressed in radar outputs are located at the geographic locations;generating a score representative of a likelihood of a tracked objectbeing at the geographic location, the score being generated based on thedata from the radar output for the geographic location, data from asecond output for the geographic location, and the prior data for thegeographic location, wherein the second output is generated by a secondsensor system of the autonomous vehicle, the second sensor system is adifferent type of sensor system as compared to the radar sensor system,and the data from the radar output for the geographic locationcorresponding to the static object is selectively suppressed forgenerating the score representative of the likelihood of the trackedobject being at the geographic location based on whether the staticobject is a predefined static object to be suppressed as specified bythe prior data for the geographic location; and controlling at least oneof the engine, the braking system, or the steering system based upon thescore representative of the likelihood of the tracked object being atthe geographic location.